Breathing Assistance Digital Twin for Predictive Airflow Personalization
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Solution Overview
Problem
Existing breathing assistance devices are reactive and not proactive in predicting and preventing respiratory distress or discomfort, often causing harm due to pressure or flow stress on the respiratory system, and lack the ability to predict imminent respiratory failure events.
Innovation Solution
A personalized predictive model is generated by re-training a trained model using false negative data to improve prediction accuracy, adjusting airflow based on sensor data, and simulating user and device states to optimize device operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If breathing assistance devices provide pressure or flow support to maintain respiratory functions, then respiratory support is improved, but user harm and distress increase due to stress or strain on the respiratory system
Solution Approach 1:
The system performs preliminary actions by continuously monitoring sensor data and using machine learning models to predict imminent respiratory failure events before they occur. This allows the device to proactively adjust pressure and flow parameters to prevent respiratory distress, rather than reactively responding after the distress has already caused harm to the user.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from the user's respiratory system is constantly collected, analyzed by machine learning models, and used to dynamically adjust the pressure and flow support parameters. This closed-loop control ensures that the device responds to actual user needs in real-time, optimizing respiratory support while minimizing harmful stress on the respiratory system.
2Productivity
If breathing assistance devices operate with standard predictive models, then general respiratory monitoring is achieved, but prediction accuracy for individual users deteriorates due to lack of personalization
Solution Approach 1:
The system applies local quality by transitioning from a general predictive model to personalized predictive models for each individual user. The machine learning models are trained on each user's specific sensor data, creating locally optimized models that capture individual breathing patterns, physiology, and response to pressure support. This personalization significantly improves prediction accuracy for each user's specific respiratory events.
Solution Approach 2:
The system implements parameter changes by continuously updating and retraining the machine learning models with new sensor data from each user. The models adapt their parameters over time to reflect changes in the user's respiratory condition, ensuring that predictions remain accurate even as the user's physiology changes. This dynamic parameter adjustment enables the system to maintain high prediction accuracy across different respiratory states.
3Ease of operation
If breathing assistance devices use reactive control methods, then response to respiratory events is simplified, but ability to predict and prevent respiratory distress deteriorates
Solution Approach 1:
The system performs preliminary actions by using machine learning models to predict imminent respiratory failure events before they occur. The models analyze sensor data patterns to forecast respiratory distress, allowing the device to proactively adjust pressure and flow parameters in advance, thereby preventing respiratory failure rather than merely responding after it occurs.
Solution Approach 2:
The system implements self-service by autonomously monitoring sensor data, predicting respiratory events, and adjusting pressure and flow parameters without requiring manual intervention. The machine learning models continuously learn from each user's data and automatically optimize the respiratory support parameters, enabling the device to serve itself while maintaining high prediction and prevention capabilities.
Data Source
AI summary
Methods, devices and systems are described for adjusting airflow provided by a breathing assistance device to a user. In one aspect, personalized predictive models may be determined and used by the breathing assistance device to adjust the airflow to the user to improve various aspects including the user's sleep. In another aspect, models are developed for the user and the breathing assistance device to provide a digital twin to simulate the user and the device and determine if the user may develop any conditions and/or whether the device is operating properly. In another aspect, the personalized predictive model may be used in digital twin simulation results to validate performance before actual deployment.